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Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users' engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site's relevance and reputation into jeopardy. Hence, it is important to understand users' participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users' engagement and their privacy practices in SO. Particularly, it analyses developers' self-disclosure behaviour regarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). Results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers' engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO. [ABSTRACT FROM AUTHOR]

Stack Overflow provides a platform for developers to seek suitable solutions by asking questions and receiving answers on various topics. However, many questions are usually not answered quickly enough. Since the questioners are eager to know the specific time interval at which a question can be answered, it becomes an important task for Stack Overflow to feedback the answer time to the question. To address this issue, we propose a model for predicting the answer time of questions, named Predicting Answer Time (i.e., PAT model), which consists of two parts: a feature acquisition and fusion model, and a deep neural network model. The framework uses a variety of features mined from questions in Stack Overflow, including the question description, question title, question tags, the creation time of the question, and other temporal features. These features are fused and fed into the deep neural network to predict the answer time of the question. As a case study, post data from Stack Overflow are used to assess the model. We use traditional regression algorithms as the baselines, such as Linear Regression, K-Nearest Neighbors Regression, Support Vector Regression, Multilayer Perceptron Regression, and Random Forest Regression. Experimental results show that the PAT model can predict the answer time of questions more accurately than traditional regression algorithms, and shorten the error of the predicted answer time by nearly 10 hours. [ABSTRACT FROM AUTHOR]

The emerging trend in technical research is to use client-generated data composed of community media to survey community opinion and scientific communication on employment and care issues. This study gathered data from the social website questions and answers and the indirect effects of stack to survey the key factors that influence public preferences in technical knowledge and opinions. Using a web search engine, topic modeling, and regression data modeling, this study quantitatively analyzed the effects of the textual and auxiliary functions of the response on the number of votes received with the response. Compared to previous surveys based on open assessments, the model results show that Quora users are more likely to talk only about technology. It may fail when query keywords do not match the text content of large documents containing relevant questions from the existing methods. Furthermore, consumers are generally not experts and provide ambiguous queries (Q&As) that lead to mixed results and face a problem with existing methods. To solve these problems, the researchers attempted to reorganize the primary results and present advanced distributed topic modeling techniques to address technologies and platforms by increasing the attributes and the time and space required to generate the model. This work briefly describes the public question-and-answer structure around the world and follows the development of the main themes of housing and employment opportunities for next-generation technologies worldwide in real-time scrolling. [ABSTRACT FROM AUTHOR]

On Stack Overflow, users reuse 11,926,354 external links to share the resources hosted outside the Stack Overflow website. The external links connect to the existing programming-related knowledge and extend the crowdsourced knowledge on Stack Overflow. Some of the external links, so-called as repeated external links, can be shared for multiple times. We observe that 82.5% of the link sharing activities (i.e., sharing links in any question, answer, or comment) on Stack Overflow share external resources, and 57.0% of the occurrences of the external links are sharing the repeated external links. However, it is still unclear what types of external resources are repeatedly shared. To help users manage their knowledge, we wish to investigate the characteristics of the repeated external links in knowledge sharing on Stack Overflow. In this paper, we analyze the repeated external links on Stack Overflow. We observe that external links that point to the text resources (hosted in documentation websites, tutorial websites, etc.) are repeatedly shared the most. We observe that different users repeatedly share the same knowledge in the form of repeated external links, thus increasing the maintenance effort of knowledge (e.g., update invalid links in multiple posts). The repeated external links can bring risks to the software engineering process, as 1) the same users can repeatedly share the external links for the purpose of promotion, and 2) external links can point to webpages with an overload of information that makes it difficult for users to retrieve relevant information. Our findings provide insights to Stack Overflow moderators and researchers. For example, we encourage Stack Overflow to centrally manage the commonly occurring knowledge in the form of repeated external links in order to better maintain the crowdsourced knowledge on Stack Overflow. [ABSTRACT FROM AUTHOR]

An essential part of software maintenance and evolution, refactoring is performed by developers, regardless of technology or domain, to improve the internal quality of the system, and reduce its technical debt. However, choosing the appropriate refactoring strategy is not always straightforward, resulting in developers seeking assistance. Although research in refactoring is well-established, with several studies altering between the detection of refactoring opportunities and the recommendation of appropriate code changes, little is known about their adoption in practice. Analyzing the perception of developers is critical to understand better what developers consider to be problematic in their code and how they handle it. Additionally, there is a need for bridging the gap between refactoring, as research, and its adoption in practice, by extracting common refactoring intents that are more suitable for what developers face in reality. In this study, we analyze refactoring discussions on Stack Overflow through a series of quantitative and qualitative experiments. Our results show that Stack Overflow is utilized by a diverse set of developers for refactoring assistance for a variety of technologies. Our observations show five areas that developers typically require help with refactoring– Code Optimization, Tools and IDEs, Architecture and Design Patterns, Unit Testing, and Database. We envision our findings better bridge the support between traditional (or academic) aspects of refactoring and their real-world applicability, including better tool support. [ABSTRACT FROM AUTHOR]

Design contexts are factors that shape a design, and whilst they are recognised by developers, they are typically tacit. Unlike software requirements, software engineering researchers have paid little attention to design contexts and there is little or no systematic research on how design contexts influence design. In this paper, we conduct an empirical investigation using Stack Overflow with the aim of mining design context knowledge that is related to design patterns. We chose to study design patterns because they are clear and identifiable. In this work, we develop a new taxonomy of design context terms related to design patterns. We introduce a new automated mining approach, DPC Miner, for mining design context knowledge from Stack Overflow. Finally, we analyse the Stack Overflow posts and present how design context impacts decisions about design patterns in practice. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution has considerably sped up the pace of skill changes in many professional domains, with scores of new skills emerging and many old skills moving towards obsolescence. For these domains, identifying the new necessary skills in a timely manner is a difficult task, where existing methods are inadequate. Understanding the process, by which these new skills and technologies appear in and diffuse through a professional domain, could give training providers more time to identify these new skills and react. For this purpose, in the present work, we look at the dynamics between online learning platforms and online hiring platforms in the software programming profession, a rapidly evolving domain. To do so, we fuse four data sources together: Stack Overflow, an online community questions and answers (Q&A) platform; Google Trends, which provides online search trends from Google; Udemy, a platform offering skill-based Massively Open Online Courses (MOOCs) where anyone can create courses; and Stack Overflow Jobs, a job ad platform. We place these platforms along two axes: i) how much expertise it takes, on average, to create content on them, and ii) whether, in general, the decision to create content on them is made by individuals or by groups. Our results show that the topics under study have a systematic tendency to appear earlier on platforms where content creation requires (on average) less expertise and is done more individually, rather than by groups: Stack Overflow is found to be more agile than Udemy, which is itself more agile than Stack Overflow Jobs (Google Trends did not prove usable due to extreme data sparsity). However, our results also show that this tendency is not present for all new skills, and that the software programming profession as a whole is remarkably agile: there are usually only a few months between the first Stack Overflow appearance of a new topic, and its first appearance on Udemy or Stack Overflow Jobs. In addition, we find that Udemy's agility has dramatically increased over time. Our novel methodology is able to provide valuable insights into the dynamics between online education and job ad platforms, enabling training program creators to look at said dynamics for various topics and to understand the pace of change. This allows them to maintain better awareness of the trends and to prioritize their attention, both on the right topics and on the right platforms. [ABSTRACT FROM AUTHOR]

Deep learning has developed rapidly in recent years, attracting the attention of numerous researchers. Since a wide range of topics are covered in this field, we are wondering what topics researchers have concerned about. However, after investigation, we find that very few researchers have paid attention to this demand. In this paper, we conduct a large-scale study to analyze the questions faced by deep learning developers. We use Stack Overflow, one of the largest question&answer sites, as our data source, and extract 32969 posts about deep learning as our studied dataset. After filtering, augmenting and pre-processing the post datasets from Stack Overflow, we use the latent Dirichlet allocation (LDA) topic model to summarize 30 topics based on their text content. In addition, we measure the difficulty and popularity of each topic, compare the different issues faced by different deep learning frameworks, and analyze the development trend of each topic. Our main results are as follows: (1) developers ask a broad spectrum of questions about deep-learning, ranging from Data Shape to Object Detection; (2) Gradient Propagation is the most popular among all the topics and (3) Object Detection is the most difficult; (4) issues of Package Installation, Code Understanding and Method Introduction are common in the current different deep learning frameworks; (5) there are three trends in these topics, e.g., a significant rising trend is found in the number of discussion on Data Shape. Finally, based on our research findings, we make some targeted and valuable suggestions for developers, researchers, educators, and framework providers of deep learning. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is defined as the connection between places and physical objects (i.e., things) over the Internet via smart computing devices. It is a rapidly emerging paradigm that encompasses almost every aspect of our modern life, such as smart home, cars, and so on. With interest in IoT growing, we observe that the IoT discussions are becoming prevalent in online developer forums, such as Stack Overflow (SO). An understanding of such discussions can offer insights into the prevalence, popularity, and difficulty of various IoT topics. For this paper, we download a large number of SO posts that contain discussions about various IoT technologies. We apply topic modeling on the textual contents of the posts. We label the topics and categorize the topics into hierarchies. We analyze the popularity and difficulty of the topics. Our study offers several findings. First, IoT developers discuss a range of topics in SO related to Hardware, Software, Network, and Tutorials. Second, secure messaging using IoT devices from the Network category is the most prevalent topic, followed by scheduling of IoT script in the Software category. Third, all the topic categories are evolving rapidly in SO, i.e., new questions are being added more and more in SO about IoT tools and techniques. Fourth, the “How” type of questions are asked more across the three topic categories (Software, Network, and Hardware), although a large number of questions are also of the “What” type: IoT developers are using SO not only to discuss how to address a problem related to IoT, but also to learn what the different IoT techniques and tools offer. Fifth, topics related to data parsing and micro-controller configuration are the most popular. Sixth, topics related to multimedia streaming and Bluetooth are the most difficult. Our study findings have implications for all four different IoT stakeholders: tool builders, developers, educators, and researchers. For example, IoT developers and newcomers can use our findings on topic popularity to learn about popular IoT techniques. Educators and researchers can make more tutorials or develop new techniques to make difficult IoT topics easier. IoT tool builders can look at our identified topics and categories to learn about IoT developers’ preferences, which then can help them develop new tools or enhance their current offerings. [ABSTRACT FROM AUTHOR]

Code maintenance data sets typically consist of a before version of the code and an after version that contains the improvement or fix. Such data sets are important for various software engineering support tools related to code maintenance, such as program repair, code recommender systems, or Application Programming Interface (API) misuse detection. Most of the current data sets are typically constructed from mining commit history in version-control systems or issues in issue-tracking systems. In this paper, we investigate whether Stack Overflow can be used as an additional source for building code maintenance data sets. Comments on Stack Overflow provide an effective way for developers to point out problems with existing answers, alternative solutions, or pitfalls. Given its crowd-sourced nature, answers are then updated to incorporate these suggestions. In this paper, we mine comment-edit pairs from Stack Overflow and investigate their potential usefulness for constructing the above data sets. These comment-edit pairs have the added benefit of having concrete descriptions/explanations of why the change is needed as well as potentially having less tangled changes to deal with. We first design a technique to extract related comment-edit pairs and then qualitatively and quantitatively investigate the nature of these pairs. We find that the majority of comment-edit pairs are not tangled, but find that only 27% of the studied pairs are potentially useful for the above applications. We categorize the types of mined pairs and find that the highest ratio of useful pairs come from those categorized as Correction, Obsolete, Flaw, and Extension. These categories can provide data for both corrective and preventative maintenance activities. To demonstrate the effectiveness of our extracted pairs, we submitted 15 pull requests to popular GitHub repositories, 10 of which have been accepted to widely used repositories such as Apache Beam () and NLTK (). Our work is the first to investigate Stack Overflow comment-edit pairs and opens the door for future work in this direction. Based on our findings and observations, we provide concrete suggestions on how to potentially identify a larger set of useful comment-edit pairs, which can also be facilitated by our shared data. [ABSTRACT FROM AUTHOR]

API tutorials are important learning resources as they explain how to use certain APIs in a given programming context. An API tutorial can be split into a number of units. Consecutive units that describe a same topic are often called a tutorial fragment. We consider the API explained by a tutorial fragment as an API tag. Generating API tags for a tutorial fragment can help understand, navigate, and retrieve the fragment. Existing approaches often do not perform well on API tag generation due to high manual effort and low accuracy. Like API tutorials, Stack Overflow (SO) is also an important learning resource that provides the explanations of APIs. Thus, SO posts also contain API tags. Besides, API tags of SO posts are abundant and can be extracted easily. In this paper, we propose a novel approach ATTACK (stands for A PI T ag for T utorial frA gments using C rowd K nowledge), which can automatically generate API tags for tutorial fragments from SO posts. ATTACK first constructs Q & A p a i r , t a g s e t pairs by extracting API tags of SO posts. Then, it trains a deep neural network with the attention mechanism to learn the semantic relatedness between Q&A pairs and the associated API tags, taking into consideration both textual descriptions and code in a Q&A pair. Finally, the trained model is used to generate API tags for tutorial fragments. We evaluate ATTACK on public Java and Android datasets containing 43,132 Q & A p a i r , t a g s e t pairs. Experimental results show that ATTACK is effective and outperforms the state-of-the-art approaches in terms of F-Measure. Our user study further confirms the effectiveness of ATTACK in generating API tags for tutorial fragments. We also apply ATTACK to document linking and the results confirm the usefulness of API tags generated by ATTACK. [ABSTRACT FROM AUTHOR]

Context: The computational linguistics community has developed tools, called wikifiers, to identify links to Wikipedia articles from free-form text. Software engineering research can leverage wikifiers to add semantic information to software artifacts. However, no empirically-grounded basis exists to choose an effective wikifier and to configure it for the software domain, on which wikifiers were not specifically trained. Objective: We conducted a study to guide the selection of a wikifier and its configuration for applications in the software domain, and to measure what performance can be expected of wikifiers. Method: We applied six wikifiers, with multiple configurations, to a sample of 500 Stack Overflow posts. We manually annotated the 41 124 articles identified by the wikifiers as correct or not to compare their precision and recall. Results: Each wikifier, in turn, achieved the highest precision, between 13% and 82%, for different thresholds of recall, from 60% to 5%. However, filtering the wikifiers’ output with a whitelist can considerably improve the precision above 79% for recall up to 30%, and above 47% for recall up to 60%. Conclusions: Results reported in each wikifier’s original article cannot be generalized to software-specific documents. Given that no wikifier performs universally better than all others, we provide empirically grounded insights to select a wikifier for different scenarios, and suggest ways to further improve their performance for the software domain. [ABSTRACT FROM AUTHOR]

Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face three major problems. First, they frequently need to read and analyse multiple results from the search engines to obtain a satisfactory solution. Second, the search is impaired due to a lexical gap between the query (task description) and the information associated with the solution (e.g., code example). Third, the retrieved solution may not be comprehensible, i.e., the code segment might miss a succinct explanation. To address these three problems, we propose CROKAGE (CrowdKnowledge Answer Generator), a tool that takes the description of a programming task (the query) as input and delivers a comprehensible solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations written by human developers. The search for code examples is modeled as an Information Retrieval (IR) problem. We first leverage the crowd knowledge stored in Stack Overflow to retrieve the candidate answers against a programming task. For this, we use a fine-tuned IR technique, chosen after comparing 11 IR techniques in terms of performance. Then we use a multi-factor relevance mechanism to mitigate the lexical gap problem, and select the top quality answers related to the task. Finally, we perform natural language processing on the top quality answers and deliver the comprehensible solutions containing both code examples and code explanations unlike earlier studies. We evaluate and compare our approach against ten baselines, including the state-of-art. We show that CROKAGE outperforms the ten baselines in suggesting relevant solutions for 902 programming tasks (i.e., queries) of three popular programming languages: Java, Python and PHP. Furthermore, we use 24 programming tasks (queries) to evaluate our solutions with 29 developers and confirm that CROKAGE outperforms the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation). [ABSTRACT FROM AUTHOR]

In common design pattern collections, e.g., design pattern books, design patterns are documented with templates that consist of multiple attributes, such as intent, structure, and sample code. To adapt to modern developers, the depictions of design patterns, especially some specific attributes, should advance with the current programming technologies, for example, "known uses", which exemplifies the use scenarios of design patterns in practice, and "related patterns", which describes the relatedness between a design pattern and the others within a context. However, it is not easy to update the contents of these attributes manually due to the diversity of the programming technologies. To address this problem, in this work, we conducted a case study to mine design pattern use scenarios and related design pattern pairs from Stack Overflow posts to enrich the two attributes. We first extracted the question posts relevant to each design pattern by identifying the design pattern tags. Then, the topics of the posts were discovered by applying topic modeling techniques. Finally, by analyzing the topics specified for each design pattern, we detected 195 design pattern use scenarios and 70 related design pattern pairs, involving 61 design patterns totally. These findings are associated with a variety of popular software frameworks and programming techniques. They could complement the existing design pattern collections and help developers better acknowledge the usage and relatedness of design patterns in today's programming practice. [ABSTRACT FROM AUTHOR]

On question and answer sites, such as Stack Overflow (SO), developers use tags to label the content of a post and to support developers in question searching and browsing. However, these tags mainly refer to technological aspects instead of the purpose of the question. Tagging questions with their purpose can add a new dimension to the identification of discussed topics in posts on SO. In this paper, we aim at automating the classification of SO question posts into seven question categories. As a first step, we harmonized existing taxonomies of question categories and then, we manually classified 1,000 SO questions according to our new taxonomy. Additionally to the question category, we marked the phrases that indicate a question category for each of the posts. We then use this data set to automate the classification of posts using two approaches. For the first approach, we manually analyzed the phrases to find patterns. Based on regular expressions, we implemented a classifier, for each of the categories, that determines whether a post belongs to a category. These regular expressions are derived by analyzing patterns in the phrases. In the second approach, we use the curated data set to train classification models of supervised machine learning algorithms (Random Forest and Support Vector Machines). For the machine learning algorithms, we experimented with 1,312 different configurations regarding the preprocessing of the text and the representation of the input data. Then, we compared the performance of the regex approach with the performance of the best configuration that uses machine learning algorithms on a validation set of 110 posts. The results show that using the regular expression approach, we can classify posts into the correct question category with an average precision and recall of 0.90, and an MCC of 0.68. Additionally, we applied the regex approach on all questions of SO that deal with Android app development and investigated the co-occurrence of question categories in posts. We found that the categories API usage, Conceptual, and Discrepancy are the most frequently assigned question categories and that they also occur together frequently. Our approach can be used to support developers in browsing SO discussions or researchers in building recommender systems based on SO. [ABSTRACT FROM AUTHOR]

The design and maintenance of APIs (Application Programming Interfaces) are complex tasks due to the constantly changing requirements of their users. Despite the efforts of their designers, APIs may suffer from a number of issues (such as incomplete or erroneous documentation, poor performance, and backward incompatibility). To maintain a healthy client base, API designers must learn these issues to fix them. Question answering sites, such as Stack Overflow (SO), have become a popular place for discussing API issues. These posts about API issues are invaluable to API designers, not only because they can help to learn more about the problem but also because they can facilitate learning the requirements of API users. However, the unstructured nature of posts and the abundance of non-issue posts make the task of detecting SO posts concerning API issues difficult and challenging. In this paper, we first develop a supervised learning approach using a Conditional Random Field (CRF), a statistical modeling method, to identify API issue-related sentences. We use the above information together with different features collected from posts, the experience of users, readability metrics and centrality measures of collaboration network to build a technique, called CAPS, that can classify SO posts concerning API issues. In total, we consider 34 features along eight different dimensions. Evaluation of CAPS using carefully curated SO posts on three popular API types reveals that the technique outperforms all three baseline approaches we consider in this study. We then conduct studies to find important features and also evaluate the performance of the CRF-based technique for classifying issue sentences. Comparison with two other baseline approaches shows that the technique has high potential. We also test the generalizability of CAPS results, evaluate the effectiveness of different classifiers, and identify the impact of different feature sets. [ABSTRACT FROM AUTHOR]

As most software systems, libraries and frameworks also evolve, which may break existing clients. However, the main reasons to introduce breaking changes in APIs are unclear. Therefore, in this paper, we first report the results of an almost 4-month long field study with popular Java libraries and frameworks. We configured an infrastructure to observe all changes in these libraries and to detect breaking changes shortly after their introduction in the code. We detected possible breaking changes in 61 projects. After identifying breaking changes, we asked the developers to explain the reasons behind their decision to change the APIs. By analyzing the developers' answers, we report that breaking changes are mostly motivated by the need to implement new features, by the desire to make the APIs simpler and with fewer elements, and to improve maintainability. To complement this first study, we describe a second study, including the analysis of 110 Stack Overflow posts related to breaking changes. We reveal that breaking changes have an important impact on clients, since 45% of the questions are from clients asking how to overcome specific breaking changes; they are also common in other ecosystems—JavaScript,.NET, etc. We conclude by providing suggestions to language designers, tool builders, software engineering researchers, and API developers. [ABSTRACT FROM AUTHOR]

Technical question and answer (Q&A) websites provide a platform for developers to communicate with each other by asking and answering questions. Stack Overflow is the most prominent of such websites. With the rapidly increasing number of questions on Stack Overflow, it is becoming difficult to get an answer to all questions and as a result, millions of questions on Stack Overflow remain unsolved. In an attempt to improve the visibility of unsolved questions, Stack Overflow introduced a bounty system to motivate users to solve such questions. In this bounty system, users can offer reputation points in an effort to encourage users to answer their question. In this paper, we study 129,202 bounty questions that were proposed by 61,824 bounty backers. We observe that bounty questions have a higher solving-likelihood than non-bounty questions. This is particularly true for long-standing unsolved questions. For example, questions that were unsolved for 100 days for which a bounty is proposed are more likely to be solved (55%) than those without bounties (1.7%). In addition, we studied the factors that are important for the solving-likelihood and solving-time of a bounty question. We found that: (1) Questions are likely to attract more traffic after receiving a bounty than non-bounty questions. (2) Bounties work particularly well in very large communities with a relatively low question solving-likelihood. (3) High-valued bounties are associated with a higher solving-likelihood, but we did not observe a likelihood for expedited solutions. Our study shows that while bounties are not a silver bullet for getting a question solved, they are associated with a higher solving-likelihood of a question in most cases. As questions that are still unsolved after two days hardly receive any traffic, we recommend that Stack Overflow users propose a bounty as soon as possible after those two days for the bounty to have the highest impact. [ABSTRACT FROM AUTHOR]

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Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers of Stack Overflow (i.e., crowdsourced knowledge). We first motivate our idea using an exploratory study with 19 standard Java API packages and 344K Java related posts from Stack Overflow. Experiments using 175 code search queries randomly chosen from three Java tutorial sites show that our technique recommends correct API classes within the Top-10 results for 83% of the queries, with 46% mean average precision and 54% recall, which are 66%, 79% and 87% higher respectively than that of the state-of-the-art. Reformulations using our suggested API classes improve 64% of the natural language queries and their overall accuracy improves by 19%. Comparisons with three state-of-the-art techniques demonstrate that RACK outperforms them in the query reformulation by a statistically significant margin. Investigation using three web/code search engines shows that our technique can significantly improve their results in the context of code search. [ABSTRACT FROM AUTHOR]

Stack Overflow (SO) is the most popular question-and-answer website for software developers, providing a large amount of copyable code snippets. Using those snippets raises maintenance and legal issues. SO's license (CC BY-SA 3.0) requires attribution, i.e., referencing the original question or answer, and requires derived work to adopt a compatible license. While there is a heated debate on SO's license model for code snippets and the required attribution, little is known about the extent to which snippets are copied from SO without proper attribution. We present results of a large-scale empirical study analyzing the usage and attribution of non-trivial Java code snippets from SO answers in public GitHub (GH) projects. We followed three different approaches to triangulate an estimate for the ratio of unattributed usages and conducted two online surveys with software developers to complement our results. For the different sets of projects that we analyzed, the ratio of projects containing files with a reference to SO varied between 3.3% and 11.9%. We found that at most 1.8% of all analyzed repositories containing code from SO used the code in a way compatible with CC BY-SA 3.0. Moreover, we estimate that at most a quarter of the copied code snippets from SO are attributed as required. Of the surveyed developers, almost one half admitted copying code from SO without attribution and about two thirds were not aware of the license of SO code snippets and its implications. [ABSTRACT FROM AUTHOR]

Technical Q&A sites have become essential for software engineers as they constantly seek help from other experts to solve their work problems. Despite their success, many questions remain unresolved, sometimes because the asker does not acknowledge any helpful answer. In these cases, an information seeker can only browse all the answers within a question thread to assess their quality as potential solutions. We approach this time-consuming problem as a binary-classification task where a best-answer prediction model is built to identify the accepted answer among those within a resolved question thread, and the candidate solutions to those questions that have received answers but are still unresolved. In this paper, we report on a study aimed at assessing 26 best-answer prediction models in two steps. First, we study how models perform when predicting best answers in Stack Overflow, the most popular Q&A site for software engineers. Then, we assess performance in a cross-platform setting where the prediction models are trained on Stack Overflow and tested on other technical Q&A sites. Our findings show that the choice of the classifier and automatied parameter tuning have a large impact on the prediction of the best answer. We also demonstrate that our approach to the best-answer prediction problem is generalizable across technical Q&A sites. Finally, we provide practical recommendations to Q&A platform designers to curate and preserve the crowdsourced knowledge shared through these sites. [ABSTRACT FROM AUTHOR]

The role of sentiment analysis is increasingly emerging to study software developers’ emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers’ communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines. [ABSTRACT FROM AUTHOR]

One of the effects of social media’s prevalence in software development is the many flourishing communities of practice where users share a common interest. These large communities use many different communication channels, but little is known about how they create, share, and curate knowledge using such channels. In this paper, we report a mixed methods study of how one community of practice, the R software development community, creates and curates knowledge associated with questions and answers (Q&A) in two of its main communication channels: the R tag in Stack Overflow and the R-Help mailing list. The results reveal that knowledge is created and curated in two main forms: participatory, where multiple users explicitly collaborate to build knowledge, and crowdsourced, where individuals primarily work independently of each other. Moreover, we take a unique approach at slicing the data based on question score and participation activities over time. Our study reveals participation patterns, showing the existence of prolific contributors: users who are active across both channels and are responsible for a large proportion of the answers, serving as a bridge of knowledge. The key contributions of this paper are: a characterization of knowledge artifacts that are exchanged by this community of practice; the reasons why users choose one channel over the other; and insights on the community participation patterns, which indicate an evolution of the community and a shift from knowledge creation to knowledge curation. [ABSTRACT FROM AUTHOR]

Developers often require knowledge beyond the one they possess, which boils down to asking co-workers for help or consulting additional sources of information, such as Application Programming Interfaces (API) documentation, forums, and Q&A websites. However, it requires time and energy to formulate one's problem, peruse and process the results. We propose a novel approach that, given a context in the Integrated Development Environment (IDE), automatically retrieves pertinent discussions from Stack Overflow, evaluates their relevance using a multi-faceted ranking model, and, if a given confidence threshold is surpassed, notifies the developer. We have implemented our approach in Prompter, an Eclipse plug-in. Prompter was evaluated in two empirical studies. The first study was aimed at evaluating Prompter's ranking model and involved 33 participants. The second study was conducted with 12 participants and aimed at evaluating Prompter's usefulness when supporting developers during development and maintenance tasks. Since Prompter uses 'volatile information' crawled from the web, we also replicated Study I after one year to assess the impact of such a 'volatility' on recommenders like Prompter. Our results indicate that (i) Prompter recommendations were positively evaluated in 74 % of the cases on average, (ii) Prompter significantly helps developers to improve the correctness of their tasks by 24 % on average, but also (iii) 78 % of the provided recommendations are 'volatile' and can change at one year of distance. While Prompter revealed to be effective, our studies also point out issues when building recommenders based on information available on online forums. [ABSTRACT FROM AUTHOR]

Security has always been a popular and critical topic. With the rapid development of information technology, it is always attracting people's attention. However, since security has a long history, it covers a wide range of topics which change a lot, from classic cryptography to recently popular mobile security. There is a need to investigate security-related topics and trends, which can be a guide for security researchers, security educators and security practitioners. To address the above-mentioned need, in this paper, we conduct a large-scale study on security-related questions on Stack Overflow. Stack Overflow is a popular on-line question and answer site for software developers to communicate, collaborate, and share information with one another. There are many different topics among the numerous questions posted on Stack Overflow and security-related questions occupy a large proportion and have an important and significant position. We first use two heuristics to extract from the dataset the questions that are related to security based on the tags of the posts. And then we use an advanced topic model, Latent Dirichlet Allocation (LDA) tuned using Genetic Algorithm (GA), to cluster different security-related questions based on their texts. After obtaining the different topics of security-related questions, we use their metadata to make various analyses. We summarize all the topics into five main categories, and investigate the popularity and difficulty of different topics as well. Based on the results of our study, we conclude several implications for researchers, educators and practitioners. [ABSTRACT FROM AUTHOR]

The popularity of mobile devices has been steadily growing in recent years. These devices heavily depend on software from the underlying operating systems to the applications they run. Prior research showed that mobile software is different than traditional, large software systems. However, to date most of our research has been conducted on traditional software systems. Very little work has focused on the issues that mobile developers face. Therefore, in this paper, we use data from the popular online Q&A site, Stack Overflow, and analyze 13,232,821 posts to examine what mobile developers ask about. We employ Latent Dirichlet allocation-based topic models to help us summarize the mobile-related questions. Our findings show that developers are asking about app distribution, mobile APIs, data management, sensors and context, mobile tools, and user interface development. We also determine what popular mobile-related issues are the most difficult, explore platform specific issues, and investigate the types (e.g., what, how, or why) of questions mobile developers ask. Our findings help highlight the challenges facing mobile developers that require more attention from the software engineering research and development communities in the future and establish a novel approach for analyzing questions asked on Q&A forums. [ABSTRACT FROM AUTHOR]

Stack Overflow is a popular on-line question and answer site for software developers to share their experience and expertise. Among the numerous questions posted in Stack Overflow, two or more of them may express the same point and thus are duplicates of one another. Duplicate questions make Stack Overflow site maintenance harder, waste resources that could have been used to answer other questions, and cause developers to unnecessarily wait for answers that are already available. To reduce the problem of duplicate questions, Stack Overflow allows questions to be manually marked as duplicates of others. Since there are thousands of questions submitted to Stack Overflow every day, manually identifying duplicate questions is a difficult work. Thus, there is a need for an automated approach that can help in detecting these duplicate questions. To address the above-mentioned need, in this paper, we propose an automated approach named DupPredictor that takes a new question as input and detects potential duplicates of this question by considering multiple factors. DupPredictor extracts the title and description of a question and also tags that are attached to the question. These pieces of information (title, description, and a few tags) are mandatory information that a user needs to input when posting a question. DupPredictor then computes the latent topics of each question by using a topic model. Next, for each pair of questions, it computes four similarity scores by comparing their titles, descriptions, latent topics, and tags. These four similarity scores are finally combined together to result in a new similarity score that comprehensively considers the multiple factors. To examine the benefit of DupPredictor, we perform an experiment on a Stack Overflow dataset which contains a total of more than two million questions. The result shows that DupPredictor can achieve a recall-rate@20 score of 63.8%. We compare our approach with the standard search engine of Stack Overflow, and DupPredictor improves its recall-rate@10 score by 40.63%. We also compare our approach with approaches that only use title, description, topic, and tag similarity and Runeson et al.'s approach that has been used to detect duplicate bug reports, and DupPredictor improves their recall-rate@10 scores by 27.2%, 97.4%, 746.0%, 231.1%, and 16.4% respectively. [ABSTRACT FROM AUTHOR]

Writable XOR executable (W⊕X) and address space layout randomisation (ASLR) have elevated the understanding necessary to perpetrate buffer overflow exploits[1]. However, they have not proved to be a panacea[1-3], and so other mechanisms, such as stack guards and prelinking, have been introduced. In this paper, we show that host-based protection still does not offer a complete solution. To demonstrate the protection inadequacies, we perform an over the network brute force return-to-libc attack against a preforking concurrent server to gain remote access to a shell. The attack defeats host protection including W⊕X and ASLR. We then demonstrate that deploying a network intrusion detection systems (NIDS) with appropriate signatures can detect this attack efficiently. [ABSTRACT FROM AUTHOR]

Online knowledge production sites, such as Wikipedia and Stack Overflow, are dominated by small groups of contributors. How does this affect knowledge quality and production? Does the persistent presence of some key contributors among the most productive members improve the quality of the knowledge, considered in the aggregate? The paper addresses these issues by correlating week-by-week value changes in contribution unevenness, elite resilience (stickiness), and content quality. The goal is to detect if and how changes in social structural variables may influence the quality of the knowledge produced by two representative online knowledge production sites: Wikipedia and Stack Overflow. Regression analysis shows that on Stack Overflow both unevenness and elite stickiness have a curvilinear effect on quality. Quality is optimized at specific levels of elite stickiness and unevenness. At the same time, on Wikipedia, quality increases linearly with a decline in entropy, overall, and with an increase in stickiness in the maturation phase, after an entropy elite stickiness, quality of content peak is reached. [ABSTRACT FROM AUTHOR]